{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T15:16:53Z","timestamp":1743088613679,"version":"3.40.3"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031147135"},{"type":"electronic","value":"9783031147142"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-14714-2_14","type":"book-chapter","created":{"date-parts":[[2022,8,13]],"date-time":"2022-08-13T21:03:13Z","timestamp":1660424593000},"page":"192-206","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["BBE: Basin-Based Evaluation of\u00a0Multimodal Multi-objective Optimization Problems"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3571-667X","authenticated-orcid":false,"given":"Jonathan","family":"Heins","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3921-0107","authenticated-orcid":false,"given":"Jeroen","family":"Rook","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3929-7465","authenticated-orcid":false,"given":"Lennart","family":"Sch\u00e4permeier","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2862-1418","authenticated-orcid":false,"given":"Pascal","family":"Kerschke","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4121-4668","authenticated-orcid":false,"given":"Jakob","family":"Bossek","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9788-8282","authenticated-orcid":false,"given":"Heike","family":"Trautmann","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,14]]},"reference":[{"key":"14_CR1","doi-asserted-by":"publisher","unstructured":"Bossek, J.: ECR 2.0: a modular framework for evolutionary computation in R. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) Companion, pp. 1187\u20131193. ACM (2017). https:\/\/doi.org\/10.1145\/3067695.3082470","DOI":"10.1145\/3067695.3082470"},{"key":"14_CR2","doi-asserted-by":"publisher","unstructured":"Bossek, J.: smoof: Single- and multi-objective optimization test functions. R J. 9(1), 103\u2013113 (2017). https:\/\/doi.org\/10.32614\/RJ-2017-004","DOI":"10.32614\/RJ-2017-004"},{"key":"14_CR3","unstructured":"Bossek, J., Deb, K.: omnioptr: Omni-Optimizer (2021). , R package version 1.0.0: https:\/\/github.com\/jakobbossek\/omnioptr"},{"issue":"2","key":"14_CR4","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1109\/4235.996017","volume":"6","author":"K Deb","year":"2002","unstructured":"Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. (TEVC) 6(2), 182\u2013197 (2002). https:\/\/doi.org\/10.1109\/4235.996017","journal-title":"IEEE Trans. Evol. Comput. (TEVC)"},{"key":"14_CR5","doi-asserted-by":"publisher","unstructured":"Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable test problems for evolutionary multiobjective optimization. In: Abraham, A., Jain, L., Goldberg, R. (eds.) Evolutionary Multiobjective Optimization. Advanced Information and Knowledge Processing. Springer, London (2005). https:\/\/doi.org\/10.1007\/1-84628-137-7_6","DOI":"10.1007\/1-84628-137-7_6"},{"key":"14_CR6","doi-asserted-by":"publisher","first-page":"1062","DOI":"10.1016\/j.ejor.2006.06.042","volume":"185","author":"K Deb","year":"2008","unstructured":"Deb, K., Tiwari, S.: Omni-optimizer: a generic evolutionary algorithm for single and multi-objective optimization. Eur. J. Oper. Res. (EJOR) 185, 1062\u20131087 (2008). https:\/\/doi.org\/10.1016\/j.ejor.2006.06.042","journal-title":"Eur. J. Oper. Res. (EJOR)"},{"key":"14_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1007\/978-3-540-31880-4_5","volume-title":"Evolutionary Multi-Criterion Optimization","author":"M Emmerich","year":"2005","unstructured":"Emmerich, M., Beume, N., Naujoks, B.: An EMO algorithm using the hypervolume measure as selection criterion. In: Coello Coello, C.A., Hern\u00e1ndez Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 62\u201376. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/978-3-540-31880-4_5"},{"key":"14_CR8","doi-asserted-by":"publisher","unstructured":"Fieldsend, J.E., Chugh, T., Allmendinger, R., Miettinen, K.: A feature rich distance-based many-objective visualisable test problem generator. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp. 541\u2013549. ACM (2019). https:\/\/doi.org\/10.1145\/3321707.3321727","DOI":"10.1145\/3321707.3321727"},{"key":"14_CR9","doi-asserted-by":"publisher","unstructured":"Grimme, C., et a.: Peeking beyond peaks: challenges and research potentials of continuous multimodal multi-objective optimization. Comput. Oper. Res. (COR) 136, 105489 (2021). https:\/\/doi.org\/10.1016\/j.cor.2021.105489","DOI":"10.1016\/j.cor.2021.105489"},{"key":"14_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1007\/978-3-030-12598-1_11","volume-title":"Evolutionary Multi-Criterion Optimization","author":"C Grimme","year":"2019","unstructured":"Grimme, C., Kerschke, P., Trautmann, H.: Multimodality in multi-objective optimization \u2013 more boon than bane? In: Deb, K., et al. (eds.) EMO 2019. LNCS, vol. 11411, pp. 126\u2013138. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-12598-1_11"},{"key":"14_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1007\/978-3-642-25566-3_40","volume-title":"Learning and Intelligent Optimization","author":"F Hutter","year":"2011","unstructured":"Hutter, F., Hoos, H.H., Leyton-Brown, K.: Sequential model-based optimization for general algorithm configuration. In: Coello, C.A.C. (ed.) LION 2011. LNCS, vol. 6683, pp. 507\u2013523. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-25566-3_40"},{"key":"14_CR12","doi-asserted-by":"publisher","unstructured":"Ishibuchi, H., Peng, Y., Shang, K.: A scalable multimodal multiobjective test problem. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC), pp. 310\u2013317. IEEE (2019). https:\/\/doi.org\/10.1109\/CEC.2019.8789971","DOI":"10.1109\/CEC.2019.8789971"},{"key":"14_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1007\/978-3-319-54157-0_23","volume-title":"Evolutionary Multi-Criterion Optimization","author":"P Kerschke","year":"2017","unstructured":"Kerschke, P., Grimme, C.: An expedition to multimodal multi-objective optimization landscapes. In: Trautmann, H., et al. (eds.) EMO 2017. LNCS, vol. 10173, pp. 329\u2013343. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-54157-0_23"},{"key":"14_CR14","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1162\/evco_a_00242","volume":"27","author":"P Kerschke","year":"2019","unstructured":"Kerschke, P., Hoos, H.H., Neumann, F., Trautmann, H.: Automated algorithm selection: survey and perspectives. Evol. Comput. (ECJ) 27, 3\u201345 (2019). https:\/\/doi.org\/10.1162\/evco_a_00242","journal-title":"Evol. Comput. (ECJ)"},{"key":"14_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"962","DOI":"10.1007\/978-3-319-45823-6_90","volume-title":"Parallel Problem Solving from Nature \u2013 PPSN XIV","author":"P Kerschke","year":"2016","unstructured":"Kerschke, P., et al.: Towards analyzing multimodality of continuous multiobjective landscapes. In: Handl, J., Hart, E., Lewis, P.R., L\u00f3pez-Ib\u00e1\u00f1ez, M., Ochoa, G., Paechter, B. (eds.) PPSN 2016. LNCS, vol. 9921, pp. 962\u2013972. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-45823-6_90"},{"key":"14_CR16","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1162\/evco_a_00234","volume":"27","author":"P Kerschke","year":"2019","unstructured":"Kerschke, P., et al.: Search dynamics on multimodal multi-objective problems. Evol. Comput. (ECJ) 27, 577\u2013609 (2019). https:\/\/doi.org\/10.1162\/evco_a_00234","journal-title":"Evol. Comput. (ECJ)"},{"key":"14_CR17","unstructured":"Li, X., Engelbrecht, A.P., Epitropakis, M.G.: Benchmark functions for cec\u20192013 special session and competition on niching methods for multimodal function optimization. Technical report, Evolutionary Computation and Machine Learning Group, RMIT University, Australia (2013). http:\/\/goanna.cs.rmit.edu.au\/~xiaodong\/cec13-niching\/competition\/"},{"key":"14_CR18","doi-asserted-by":"publisher","unstructured":"Maree, S.C., Alderliesten, T., Bosman, P.A.N.: Real-valued evolutionary multi-modal multi-objective optimization by Hill-Valley clustering. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp. 568\u2013576. ACM (2019). https:\/\/doi.org\/10.1145\/3321707.3321759","DOI":"10.1145\/3321707.3321759"},{"key":"14_CR19","doi-asserted-by":"publisher","unstructured":"Miettinen, K.: Nonlinear Multiobjective Optimization. International Series in Operation Research and Management Science, vol. 12. Springer, Heidelberg (1998). https:\/\/doi.org\/10.1007\/978-1-4615-5563-6","DOI":"10.1007\/978-1-4615-5563-6"},{"key":"14_CR20","unstructured":"Nemenyi, P.B.: Distribution-free multiple comparisons. Ph.D. thesis, Princeton University (1963)"},{"key":"14_CR21","doi-asserted-by":"publisher","unstructured":"Preuss, M.: Multimodal Optimization by Means of Evolutionary Algorithms. Natural Computing Series (NCS). Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-07407-8","DOI":"10.1007\/978-3-319-07407-8"},{"key":"14_CR22","doi-asserted-by":"publisher","unstructured":"Preuss, M., Wessing, S.: Measuring multimodal optimization solution sets with a view to multiobjective techniques. In: Emmerich, M. et al. (eds.) EVOLVE - A Bridge Between Probability, Set Oriented Numerics, and Evolutionary Computation IV, pp. 123\u2013137. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-319-01128-8_9","DOI":"10.1007\/978-3-319-01128-8_9"},{"key":"14_CR23","doi-asserted-by":"publisher","unstructured":"Rook, J., Trautmann, H., Bossek, J., Grimme, C.: On the potential of automated algorithm configuration on multi-modal multi-objective optimization problems. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) Companion. p. tbd. ACM (2022). https:\/\/doi.org\/10.1145\/3520304.3528998, accepted","DOI":"10.1145\/3520304.3528998"},{"key":"14_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1007\/978-3-030-58115-2_11","volume-title":"Parallel Problem Solving from Nature \u2013 PPSN XVI","author":"L Sch\u00e4permeier","year":"2020","unstructured":"Sch\u00e4permeier, L., Grimme, C., Kerschke, P.: One PLOT to show them all: visualization of efficient sets in multi-objective landscapes. In: B\u00e4ck, T., et al. (eds.) PPSN 2020. LNCS, vol. 12270, pp. 154\u2013167. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58115-2_11"},{"key":"14_CR25","doi-asserted-by":"publisher","unstructured":"Sch\u00e4permeier, L., Grimme, C., Kerschke, P.: To boldly show what no one has seen before: a dashboard for visualizing multi-objective landscapes. In: Proceedings of the International Conference on Evolutionary Multi-criterion Optimization (EMO), pp. 632\u2013644 (2021). https:\/\/doi.org\/10.1007\/978-3-030-72062-9_50","DOI":"10.1007\/978-3-030-72062-9_50"},{"key":"14_CR26","doi-asserted-by":"publisher","unstructured":"Sch\u00e4permeier, L., Grimme, C., Kerschke, P.: MOLE: digging tunnels through multimodal multi-objective landscapes. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO). p. tbd. ACM (2022). https:\/\/doi.org\/10.1145\/3512290.3528793, accepted","DOI":"10.1145\/3512290.3528793"},{"key":"14_CR27","unstructured":"Sch\u00e4permeier, L.: An R package implementing the multi-objective landscape explorer (MOLE), February 2022. https:\/\/github.com\/schaepermeier\/moleopt"},{"key":"14_CR28","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/BF02426650","volume":"1","author":"AR Solow","year":"1994","unstructured":"Solow, A.R., Polasky, S.: Measuring biological diversity. Environ. Ecol. Stat. 1, 95\u2013103 (1994). https:\/\/doi.org\/10.1007\/BF02426650","journal-title":"Environ. Ecol. Stat."},{"key":"14_CR29","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.swevo.2019.06.001","volume":"49","author":"R Tanabe","year":"2019","unstructured":"Tanabe, R., Ishibuchi, H.: A Niching indicator-based multi-modal many-objective optimizer. Swarm Evol. Comput. (SWEVO) 49, 134\u2013146 (2019). https:\/\/doi.org\/10.1016\/j.swevo.2019.06.001","journal-title":"Swarm Evol. Comput. (SWEVO)"},{"issue":"2","key":"14_CR30","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1109\/TEVC.2014.2313407","volume":"19","author":"T Tu\u0161ar","year":"2015","unstructured":"Tu\u0161ar, T., Filipi\u010d, B.: Visualization of pareto front approximations in evolutionary multiobjective optimization: a critical review and the prosection method. IEEE Trans. Evol. Comput. (TEVC) 19(2), 225\u2013245 (2015). https:\/\/doi.org\/10.1109\/TEVC.2014.2313407","journal-title":"IEEE Trans. Evol. Comput. (TEVC)"},{"key":"14_CR31","doi-asserted-by":"publisher","unstructured":"Tu\u0161ar, T., Brockhoff, D., Hansen, N., Auger, A.: COCO: the bi-objective black box optimization benchmarking (BBOB-BIOBJ) test suite. arXiv preprint abs\/1604.00359 (2016). https:\/\/doi.org\/10.48550\/arXiv.1604.00359","DOI":"10.48550\/arXiv.1604.00359"},{"key":"14_CR32","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1007\/978-3-642-15844-5_71","volume-title":"Parallel Problem Solving from Nature, PPSN XI","author":"T Ulrich","year":"2010","unstructured":"Ulrich, T., Bader, J., Thiele, L.: Defining and optimizing indicator-based diversity measures in multiobjective search. In: Schaefer, R., Cotta, C., Ko\u0142odziej, J., Rudolph, G. (eds.) PPSN 2010. LNCS, vol. 6238, pp. 707\u2013717. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-15844-5_71"},{"key":"14_CR33","doi-asserted-by":"publisher","unstructured":"Ulrich, T., Thiele, L.: Maximizing population diversity in single-objective optimization. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp. 641\u2013648. ACM (2011). https:\/\/doi.org\/10.1145\/2001576.2001665","DOI":"10.1145\/2001576.2001665"},{"key":"14_CR34","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.swevo.2019.03.011","volume":"48","author":"C Yue","year":"2019","unstructured":"Yue, C., Qu, B., Yu, K., Liang, J., Li, X.: A novel scalable test problem suite for multimodal multiobjective optimization. Swarm Evol. Comput. 48, 62\u201371 (2019). https:\/\/doi.org\/10.1016\/j.swevo.2019.03.011","journal-title":"Swarm Evol. Comput."},{"issue":"2","key":"14_CR35","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1162\/106365600568202","volume":"8","author":"E Zitzler","year":"2000","unstructured":"Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: empirical results. Evol. Comput. (ECJ) 8(2), 173\u2013195 (2000). https:\/\/doi.org\/10.1162\/106365600568202","journal-title":"Evol. Comput. (ECJ)"},{"key":"14_CR36","doi-asserted-by":"publisher","unstructured":"Zitzler, E., Thiele, L.: Multiobjective optimization using evolutionary algorithms - a comparative case study. In: Eiben, A.E., B\u00e4ck, T., Schoenauer, M., Schwefel, H.P. (eds.) PPSN 1998. LLNCS, vol. 1498. pp. 292\u2013301. Springer, Heidelberg (1998). https:\/\/doi.org\/10.1007\/bfb0056872","DOI":"10.1007\/bfb0056872"},{"issue":"2","key":"14_CR37","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1109\/TEVC.2003.810758","volume":"7","author":"E Zitzler","year":"2003","unstructured":"Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., da Fonseca, V.G.: Performance assessment of multiobjective optimizers: an analysis and review. IEEE Trans. Evol. Comput. (TEVC) 7(2), 117\u2013132 (2003). https:\/\/doi.org\/10.1109\/TEVC.2003.810758","journal-title":"IEEE Trans. Evol. Comput. (TEVC)"}],"container-title":["Lecture Notes in Computer Science","Parallel Problem Solving from Nature \u2013 PPSN XVII"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-14714-2_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T16:42:25Z","timestamp":1710261745000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-14714-2_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031147135","9783031147142"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-14714-2_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"14 August 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PPSN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Parallel Problem Solving from Nature","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Dortmund","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ppsn2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ppsn2022.cs.tu-dortmund.de\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"185","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"85","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"46% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.75","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.11","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}